| KEYWORD |
Integrate big data processing and management, especially based on long-term monitoring practice
Thesis abroad
Reference persons GIAN PAOLO CIMELLARO
Description Due to the significant complexity and variability of structural health monitoring data, current technology can only reasonably assess limited amounts of information. Yet, as structural health monitoring expands to more complex problems and its data includes hundreds of terabytes of structural data over several decades, big data strategies become essential.
This research will integrate statistical, big data strategies with structural health monitoring systems. The new, integrated framework will enable the tracking and the prediction of structural damage in harsh, highly variable environments over many years of monitoring. The big data structural health monitoring framework is based on dynamic time warping, singular value decomposition, factor analysis, and maximum likelihood statistics. The big data structural health monitoring framework is tested through several short-term (hours to days) and long-term (multi-year) structural health monitoring campaigns of infrastructure subject to different damage and environmental conditions.
Deadline 17/11/2023
PROPONI LA TUA CANDIDATURA